Breast cancer is a common cancer of women and a common cause of cancer deaths. An effective way to improve prognosis and survival rate is early detection and treatment of breast cancer. Mammography is an imaging modality which has provided some effectiveness in the early detection of clinically occult breast cancer, and is viewed by some to be a primary imaging modality for breast cancer screening.
Mammography combined with ultrasound (sonography) examination is considered by some to be an effective method for early diagnosis of breast cancers. As an adjunct to mammography for breast cancer detection and diagnosis, ultrasound can be used to determine whether a detected mass from screening mammography is solid or cystic. The characteristics of the lesion extracted from ultrasound images could also assist in differentiating between benign and malignant lesions. Refer for example to A T Stavros et al., “Solid Breast Nodules: Use Of Sonography To Distinguish Between Benign And Malignant Lesions”, Radiology, Vol. 196, pp. 123-134, 1995. See also Parker S L, Tong T, Bolden S and Wingo P A. Cancer Statistics. Ca Cancer J Clin 1997; 47:5-27.
Currently, mammography is believed to achieve a reported sensitivity (i.e., a fraction of breast cancers that are detected by mammography) of 85%-95%. Despite improved radiographic criteria for differentiating malignant from benign lesions of the breast, misclassification of lesions can occur in everyday clinical practice. Refer to the following references.    Anant Madabhushi et al, “Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasound breast images”, IEEE Transactions on Medical Imaging, Vol. 22, No. 2, pp 155-169, February 2003.    Segyeony Joo et al, “Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features”, IEEE Transactions on Medical Imaging, Vol. 23, No. 10, pp. 1292-1300, October 2004.    A. Hammoude, “An Empirical Parameter Selection Method for Endocardial Border Identification Algorithm”, Computerized Medical Imaging and Graphics, Vol. 25, pp. 33-45, 2001.    Bassett L W and Gold R H. Breast Cancer Detection: Mammography and Other Methods in Breast Imaging. New York, Grune & Stratton, 1987.    D'Orsi C J and Kopans D B. Mammographic feature analysis. Seminars in Roentgenology 1993; 28:204-230.    D'Orsi C J, Swets J A, Pickett R M, Seltzer S E and McNeil B J. Reading and decision aids for improved accuracy and standardization of mammographic diagnosis. Radiology 1992; 184:619-622.    Knutzen A M and Grisvold J J. Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clin Proc 1993; 68:454-460.    Sickles E A. Periodic mammographic follow-up of probably benign lesions: results in 3184 consecutive cases. Radiology 1991; 179:463-468.    Kopans D B. Breast Imaging. Philadelphia, Lipincott, 1989.
It has been estimated that only 15-30% of mammographic lesions sent to biopsy are actually malignant. Variability (estimated as 7% to 40%) in positive biopsy rates between individual radiologists has also been reported. Thus, use of ultrasound images adjunct to mammography is believed to be increasingly important to reduce the number of benign cases sent for unnecessary biopsy.
In addition, there is a need for an objective computerized classification scheme adapted to differentiate between benign and malignant masses at the level similar to experienced radiologists to promote improvement in the diagnostic accuracy of less-experienced radiologists, to further promote the reduction in the number of unnecessary biopsies for benign lesions.
U.S. Pat. No. 5,984,870 (Giger) is directed to a method and system for the analysis of a lesion existing in anatomical tissue.
U.S. Patent Application No. 2003/0161513 (Drukker) is directed to the analysis of lesion shadows in an ultrasound image.
U.S. Pat. No. 6,855,114 (Drukker) is directed to a radial gradient index (RGI) feature in a sonographic image.
A difficulty which has been associated with a computerized system for detecting and diagnosing breast lesions is the segmentation of the lesion regions from the surrounding tissues. In some systems, the segmentation is accomplished by manually outlining the lesions using a graphic user interface, for example, U.S. Pat. No. 5,984,870 (Giger). This manual procedure is labor-intensive, can disrupt full automation, and can be prone to human error, inconsistency, and subjectivity.
Accordingly, there exists a need for an automated segmentation module for a computerized mammography analysis system. Accurate segmentation of a breast lesion is an important step to ensure accurate classification of a detected breast lesion as a benign or malignant lesion. Further, automated segmentation of breast lesions in ultrasound images can improve the workflow by removing the manual segmentation step.
Several approaches have been proposed to segment ultrasound breast images for automated diagnosis of breast lesions. See for example, Madabhushi and Metaxas, “Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasound breast images,” IEEE Trans. on Medical Imaging, Vol. 22, No. 2, February 2003, pp. 155-169; and Horsch K, Giger M L, Venta L A and Vyborney C J., “Automatic segmentation of breast lesions on ultrasound images”, Med Phys 2001; 28:1652-1659.
Given that ultrasound images comprise speckle noise and tissue related textures, accurate segmentation task remains as a challenge.
Pixel-based, edge-based, region-based, and model-based segmentation techniques are known in medical image processing. Some approaches may have limitations. For example, pixel-based segmentation techniques tend to have difficulties when there is a significant amount of noise in the image. Edge-based techniques tend to experience problems when the boundary of the object is not well defined and when the image contrast is poor. Model-based techniques tend to fail when there is a significant amount of variation in the shape and appearance of the object of interest. Region-growing techniques require a good seed point (typically provided by manual interaction) and can be subject to errors when adjoining objects closely match an object of interest in their appearance. U.S. Patent Application No. 2003/0125621 (Drukker) describes gradient features and region growing methods to segment breast lesions in ultrasound images.
Accordingly, there exists a need for a method, which overcomes the limitations of existing methods.
Reference is made to commonly assigned application U.S. Ser. No. 10/994,794, entitled “DETECTING AND CLASSIFYING LESIONS IN ULTRASOUND IMAGES”, filed on Nov. 22, 2004 in the names of Luo et al., and which is incorporated herein by reference. The Luo et al application describes a method for detecting a lesion in a digital ultrasound image of anatomical tissue, the method comprising the steps of accessing the digital ultrasound image of anatomical tissue; segmenting spatially contiguous pixels in the digital image into a plurality of regions in accordance with substantially similar intensity patterns; selecting, from the plurality of regions, one or more candidate lesion regions having an intensity value lower than a pre-determined intensity value; and classifying the one or more candidate lesion regions into at least one of the following classes: benign, malignant, or unknown.
The present invention provides a lesion detection and segmentation wherein detection and segmentation are automatic. The method examines the similarity and dissimilarity in intensity and texture patterns of regions and identifies regions as potential candidates for breast lesions. Thus, the method is less sensitive to the noise and target appearance.